| 研究課題/領域番号 |
20K08012
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| 研究機関 | 仙台高等専門学校 |
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研究代表者 |
張 暁勇 仙台高等専門学校, 総合工学科, 准教授 (90722752)
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| 研究分担者 |
費 仙鳳 東北文化学園大学, 工学部, 准教授 (20620470)
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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| キーワード | Mammograpy / Deep Learning / Explainable AI / Computer-Aided Diagnosis / Lesion Detection |
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| 研究実績の概要 |
The purpose of this research is to develop an interpretable deep learning (DL)-based computer-aided diagnosis (CAD) system for breast cancer diagnosis in mammogram. On the base of the achivement of FY2021, we achived the following progresses in the FY2022.
(1) Experments for evaluation of DL models in lesion detection has been conducted on four mammogram data sets, which were collected in the previous FY.
(2) A new training method, which utilized the clinicians's pixel-wise anotation and saliency maps to improve the DL model acuuracy, was proposed and tested.
(3) Two papers has been published in the related international journals.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
In the FY2022, the paper publication was progressed smoothly according to the research plan. However, the CAD system development was slightly delayed since the experimental device was unavailable.
(1) Two papers about the DL for medical image analysis have been published in the FY2022. And another paper is still under reviewed currently.
(2) A GPU-equipped computer installation was delayed since the global semiconductor shortage in 2022.
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| 今後の研究の推進方策 |
According to the research plan, the main research in FY2023 will be focused on the following three tasks.
(1) Installing the GPU-equipped computer and complete the remaining experiments.
(2) Evaluating the accuracy of DL models in comparison with clinicians screening and assessing whether the screening accuracy of clinicians can be improved with the AI-aided system.
(3) A conclusive paper will be submitted to prime international journal.
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| 次年度使用額が生じた理由 |
Due to the semiconductor shortage, the delivery of GPU-equipped computers for computational purposes is being delayed. As a result, it may not be possible to complete the subsidized project within the designated period.
A GPU device will be installed and remaining experiments will be completed as soon as possibley in the FY2023.
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